ArticleScientific data2023
A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- An Open-Source Wearable System for Real-Time Human Biomechanical Analysis.Sensors (Basel, Switzerland) · 2025Article
- Mapping the complexity of motor variability: From individual space of variability to motor fingerprints.Behavior research methods · 2025Article
- NONAN GaitPrint: An IMU gait database of healthy young adults.Scientific data · 2023Article
- OperatorEYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information.Sensors (Basel, Switzerland) · 2023Article
- A Non-Laboratory Gait Dataset of Full Body Kinematics and Egocentric Vision.Scientific data · 2023Article
- Complexity of locomotion activities in an outside-of-the-lab wearable motion capture dataset.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
In this manuscript, we describe a unique dataset of human locomotion captured in a variety of out-of-the-laboratory environments captured using Inertial Measurement Unit (IMU) based wearable motion capture. The data contain full-body kinematics for walking, with and without stops, stair ambulation, obstacle course navigation, dynamic movements intended to test agility, and negotiating common obstacles in public spaces such as chairs. The dataset contains 24.2 total hours of movement data from a college student population with an approximately equal split of males to females. In addition, for one of the activities, we captured the egocentric field of view and gaze of the subjects using an eye tracker. Finally, we provide some examples of applications using the dataset and discuss how it might open possibilities for new studies in human gait analysis.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.